A Robust Weighted Association Rule Mining Using FP - Tree

The main goal of association rule mining is to examine large transaction databases which reveal implicit relationship among the data attributes. Classical association rule mining model assumes that all items have same significance without assigning their weight within a transaction or record. This proposed method gives importance for the items and transactions while calculating weight various algorithms have been represented by researchers. The proposed method combines W-support measure and the essential features of the FP-tree to reduce the time complexity. The experimental result shows that the proposed method performs better than existing method.